U.S. digital cellular (USDC) communications uses digitized voice and data signals for communication between a mobile telephone and a base station. These signals are transmitted in the form of short data bursts. When the mobile moves, it may encounter degraded communication channels due to noise and multipath distortion; both noise and distortion varying with time. The multipath distortion is due to a signal being received by the mobile at different times when it bounces off buildings and terrain. Multipath channels can cause intersymbol interference that can be removed with an adaptive equalizer, a specific type of an adaptive filter.
A typical equalizer for USDC uses an adaptive channel estimator. An adaptive channel estimator (ACE) is a linear transversal adaptive filter that attempts to model the impulse response of the communication channel. Since the ACE is a discrete time filter, it accurately attempts to model the sampled impulse response of the communication channel. Typically, the spacing of the channel estimator taps is T.sub.s, where T.sub.s is defined to be the inverse of the transmission symbol (baud) rate. This choice of spacing is very useful because it allows the detector to view the entire communication system as a discrete symbol source followed by a finite impulse response (FIR) filter and an additive noise source. This discrete time system model is illustrated in FIG. 1.
The FIR filter is represented by the equation: EQU H(n)={h.sub.i (n)}
where n denotes time, i denotes the FIR filter coefficient index, and h.sub.i (n) are the tap values of the FIR filter. The reference signal for the ACE, which is illustrated in FIG. 2, is the baseband T-sampled signal at the receiver: ##EQU1## where .alpha.(n) are the training or detected symbols that are input to the ACE from the discrete symbol source and r(n) is the additive noise.
The ACE estimates the (possibly time varying) vector H(n), representing the FIR filter coefficients. The ACE uses an adaptive algorithm, such as a recursive least square (RLS) or least mean square (LMS) algorithm, to minimize the mean square error between the reference signal and the ACE output signal, y(n). The ACE output signal, therefore, is ideally equal to the signal at point A in FIG. 1.
In many adaptive filtering problems, the RLS algorithm provides faster convergence and better tracking capability than the LMS process. However, in the case of an adaptive channel estimator, the simpler and more robust LMS process tracks channel variations as well as the RLS process. This is true because the input to the filter is random data symbols that tend to be uncorrelated. The convergence rate of the LMS process is dependent on the autocorrelation of the input signal, and since this correlation is virtually zero, the LMS converges just as rapidly as the RLS process. The configuration of the ACE is illustrated in FIG. 2. The values of the ACE taps are updated by the equation: ##EQU2## where: EQU e(n)=y(n)-X.sup.T (n)H(n-1) EQU X.sup.T (n)=[.alpha.(n).alpha.(n-1) . . . .alpha.(n-L+1)] EQU H.sup.T (n)=[h.sub.1 (n)h.sub.2 (n) . . . h.sub.L (n)]
L=Number of taps in ACE PA1 .sigma..sup.2 =Variance of .alpha.(n) PA1 .mu.=Normalized LMS update coefficient; 0.ltoreq..mu..ltoreq.1.
Note that in a QPSK type modulation scheme, such as is used in USDC, the variance of .alpha.(n) is 1, or can be normalized to a value of 1 because the amplitude of all transmitted symbols is identical.
The ACE used with a maximum likelihood sequence equalizer (MLSE) detector in the USDC receiver has more than one tap (L.gtoreq.2). When receiving static or fiat Rayleigh faded signals, only one tap should be non-zero because the signal has not undergone delay spread. The non-zero tap, typically referred to as the main tap, should have a magnitude proportional to the level of the reference signal. In reality, however, noise corrupts the reference signal, and the non-main taps also have some non-zero amplitude. This degrades the output of the channel estimator and causes the MLSE detector to perform poorly compared to a coherent detector in a static or fiat Rayleigh faded channel. There is a resulting need for a method to allow the detector to perform as well as a coherent detector in a fiat, Rayleigh faded channel.